Data-driven model-free adaptive control tuned by virtual reference feedback tuning

Abstract

This paper proposes a new tuning approach, by which, all parameters of a data-driven Model-Free Adaptive Control (MFAC) algorithm are automatically determined using a nonlinear Virtual Reference Feedback Tuning (VRFT) algorithm. The approach is referred to as mixed MFAC-VFRT control and it leads to mixed MFAC-VFRT algorithms. An advantage of mixed MFAC-VFRT control, is that it combines systematically, the features of VRFT (it computes the controller parameters using only the input/output data) with those of MFAC. This is especially illustrated by comparison with the classical MFAC algorithms, the initial values of the parameters, which are obtained through a process that involves solving an optimization problem. The application that validates the mixed MFAC-VFRT algorithms, by experiment, is a nonlinear twin rotor aerodynamic system laboratory equipment position control system, that represents a tribute, to Prof. Antal (Tony) K. Bejczy for his excellent results in space robotics, robot dynamics and control, haptics and force perception/control.